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01/20/2011 1

QbD Methodology Aids in Achieving Real Time Release of Medical Device Components

Lee Huston - Baxter

Presenter(s), Arial 18pt GrayTitle/Function/Business

© Copyright 1995 – 2011 Baxter Healthcare Corporation All rights reserved

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Topics

• Initial Process Selected for Real Time Release

• Selecting the Design Space using Scientific Method

• Developing Multivariate Model

• Implementing Model for Real Time Release


QbD & PAT in Practice

• Often monitoring & control strategies used to support QbD are derived without representative variation in the inputs

– Material Qualities (Viscosity changes)

– Environment (Material moisture content)

– Equipment (Type, condition, age, etc.)

• Including input variation in the design space allows for greater production flexibility

– Manage greater raw material variation

– Manage operator driven changes

– Reduced quality variations


Motivation for Change

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1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Injection Molding DPM'sProcess Improvement Trends / Results


Motivation for Change

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• Was tasked with continuing the trend shown above

– Improve versus maintain

– Automated versus Manual

– Science versus Black Art

• Discovered PAT and learned of what the FDA / Pharma industry was trying to accomplish

– Worked to become proficient in multivariate methods (PCA initially)

– Started following Pharma closely looking for parallels

– Attended this conference (IFPAC) regularly to understand the current state


Medical Device Components

• Typically extremely low cost

• Very High Volume

– 650+ million components per year

– Cycle time of 8 – 45 sec

– Parts per cycle: 2 - 64

• Typical Sampling Plans do not provide adequate protection against defects

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Motivation for Real Time Release


Medical Device Components

• Defect Rates are very low

• Process is typically stable & capable

• Process upsets last few cycles

• Defects found in next manufacturing step or location increase cost exponentially

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Motivation for Real Time Release

Defect Present


Process – Simple Overview

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Process Step

Typical Injection Molding Process used to produce most plastic components© Copyright 1995 – 2011 Baxter Healthcare Corporation All rights reserved

Long Term Plan

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Scientific or Decoupled process validation

Controlled Cycle Sheets & Verification

100% cavitation, 100% of the time

Correct Process Monitoring Sensors

Hourly Shot Inspections

Lean Visual Management

Steel Certification

Training

Real-Time Release

Foundation for Success

Complete

Goal


Long Term Plan

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Use Multivariate Statistics to fingerprint our processes to achieve Real Time Release

Utilization of Six-Sigma type approach for Process Understanding


Design Space for a Molding Process

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Capturing the physics of the process and translating into phases

Basic Inputs captures dynamicsNozzle TemperatureZone 1 TemperatureZone 2 TemperatureZone 3 TemperatureInjection Pressure

Screw PositionCycle Time

Fill TimePack Time Plasticate Time

Calculated Data FeaturesFill Time

Pack/Hold TimePlasticating Time

Cooling TimeCycle Time

Start of FillingScrew distance during Filling Stage

Screw Distance during Packing/Holding StageScrew distance during Cooling Stage

Average Screw Velocity during Filling StageAverage Screw Velocity during first 5% of Packing Stage

Average Screw Velocity during Packing StageAverage Screw Velocity during Plasticating

Maximum Injection Pressure during Filling StageMaximum Injection Pressure during Packing Stage

Average Injection Pressure during Filling StageAverage Injection Pressure during Packing Stage

Average Injection Pressure during Plastication StagePlastication energy measured during plastication

Average viscosity during fillingAverage viscosity during plastication

Average Nozzle TemperatureAverage Zone 1 TemperatureAverage Zone 2 TemperatureAverage Zone 3 Temperature

Integral of Injection Pressure during fillingIntegral of Injection Pressure during packing

Integral of Injection Pressure during plasticationShot Cushion

Shot SizeOther variables as needed

Calculated


Design Space for a Molding Process

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Trial Variable 1 Variable 2 Variable 3

1 -1 -1 -12 -1 -1 13 -1 1 -14 -1 1 15 1 -1 -16 1 -1 17 1 1 -18 1 1 19 0 0 0

RunVariable

1Variable

2Variable

3Variable

4Variable

5Variable

6Variable

71 -1 -1 -1 -1 -1 -1 -12 -1 1 1 -1 -1 -1 13 1 1 -1 1 -1 -1 -14 1 -1 1 1 -1 -1 15 -1 -1 -1 1 -1 1 16 -1 1 1 1 -1 1 -17 1 1 -1 -1 -1 1 18 1 -1 1 -1 -1 1 -19 0 0 0 0 0 0 010 -1 1 -1 1 1 -1 111 -1 -1 1 1 1 -1 -112 1 -1 -1 -1 1 -1 113 1 1 1 -1 1 -1 -114 -1 1 -1 -1 1 1 -115 -1 -1 1 -1 1 1 116 1 -1 -1 1 1 1 -117 1 1 1 1 1 1 1

+

Process Characterization methodology for our molding processes

Material & Process Relatedvariation Mold & Operator Related

variation


Process Model

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• Process Model developed on calculated variables and using the two previous data sets

• Simple PCA type analysis found to be adequate for our process

R2 = 97% for this particular part


Process Model

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Process Model

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Process Model - Implementation

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MVDA Engine


Process Model - Implementation

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User interface that operators are trained to use


Process Model - Validation

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Data collection system standardized for all machines

• SenseLink-QM system used

Validation / Verification process has been standardized for injection molding process

• Standard methodology for data collection

• Standard methodology for model development

• Standard methodology for verification & validation activities

Methodology is currently Mold / Machine / Stock Number specific


Process Model - Results

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Results after implementation



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Labor Hours – Quality

• Approximately 2000 hrs/year labor reduction achieved

• Production volume increase of 26%, without adding headcount– 2006 production volume = 486,463,916– 2008 production volume = 655,940,710

Exception Examples

• Greater than 90% reduction in exceptions on some machines with Real Time Release implemented



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Faster Machine startup

• 40 changeovers / month average in 2006

• 66 changeovers / month average in 2008

• Process model ensures process settings, enables faster startup



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Process Knowledge


Process Knowledge Gained

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Real Time data allows us to detect things like supplier related issues much faster



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Real Time data allows us to detect things like intermittent hardware failures


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0 1000 2000 3000 4000 5000 6000 7000

Subgroups

Target

3 SD

3 SD

LCL(EWMA)

UCL(EWMA)

Side 1 10-13-05.M1 (PCA-X)EWMA/Shewhart (Subgroup 1): XVar(Var_6)

SIMCA-P+ 11 - 10/13/2005 8:02:08 PM

S(M1) = 1.028 Target(M1) = 43.76 S(EWMA) = 0.2116 UCL(EWMA) = 44.4

LCL(EWMA) = 43.13 Lambda = 0.08125


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Impact of raw material changes

Process shifts, consistently every 2.5 – 3 hours


Summary

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Many parallels exist between Pharma & Device Manufacturing

• QbD & PAT methods can be implemented in both arenas

• Real Time Release increases process knowledge & understanding

• The information age has allowed us both access to critical data at low cost

• Real Time Release is possible in subassembly manufacturing operations

• Increased knowledge leads to safe, effective products and a better patient outcome

At the end of the day, it is really about the difference we make in our patients lives


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THANK YOU FOR YOUR ATTENTION

Lee Hutson

[email protected]


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